Orthogonal Frequency Division Multiplexing (OFDM) and multiple antenna (i.e., Multiple Input Multiple Output (MIMO)) techniques have attracted extensive attention for its high spectral efficiency in Long Term Evolution (LTE) and Institute of Electrical and Electronics Engineers (IEEE) 802.16 standards.
OFDM based systems do not suffer from interference among multiplexed users within a cell given perfect frequency synchronization. However, when it comes to a multi-cell environment, InterCell Interferences (ICIs) between adjacent cells are induced which seriously degrade cell edge user performance.
Intercell interference mitigation techniques are generally classified as three types: interference randomization, interference cancellation, and interference coordination. The interference randomization is aimed at randomizing interference and allowing interference mitigation through processing gain. The interference cancellation techniques are proposed to cancel interference at receivers by using multiple antenna techniques or Interleaved Division Multiple Access (IDMA) schemes. The interference coordination is based on the concept that a well-designed coordination of resource among Mobile Stations (MSs) in adjacent cells can reduce interference and improve user capacity and coverage at cell edge.
FIG. 1 illustrates intercell interference experienced by a cell edge MS in a MIMO system according to the conventional art.
Referring to FIG. 1, it is assumed that a serving BS 100 and an interfering BS 120 use the same codebook for precoding, and are connected with each other through a backbone network. In addition, in an environment in which the serving BS 100 and the interfering BS 120 transmit data using the same frequency band, an MS 110 located at a cell edge of the serving BS 100 suffers from interference from the interfering BS 120, which is an adjacent BS. H1 105 is a channel matrix between the serving BS 100 and the MS 110, and H2 115 is a channel matrix between the interfering BS 120 and the MS 110.
In order to mitigate interference, the MS 110 searches a reference signal for a Precoding Matrix Index (PMI) (hereinafter, referred to as ‘the worst PMI’) causing high interference or a PMI (hereinafter, referred to as ‘the best PMI’) causing low interference. The MS 110 can feed back the searched worst or best PMI to the serving BS 100 and then, the serving BS 100 informs the interfering BS 120 of the worst or best PMI fed back from the MS 110. The interfering BS 120 restricts the fed back worst PMI among its using PMIs (hereinafter, referred to as ‘PMI restriction’) or uses the fed back best PMI among its using PMIs (hereinafter, referred to as ‘PMI recommendation’), thereby being able to mitigate the interference of the MS 110.
However, the feedback of only the worst or best PMI to mitigate the interference is not enough because other PMIs besides the worst PMI may also cause interference in the MS 110, and other PMIs besides the best PMI may also work well for interference mitigation. Therefore, it is preferred if multiple PMIs can be fed back for either PMI restriction or recommendation. However, the difficulty is that a feedback of a PMI subset from the MS 110 to the serving BS 100 will incur too much overhead.
Thus, there is a need for a method and apparatus for efficient PMI feedback for interference cancellation in a multiple antenna system.